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To properly compare and contrast the environmental performance of one vehicle technology against another, it is necessary to consider their production, operation, and end-of-life fates. Since 1995, Argonne’s GREET® life cycle analysis model (Greenhouse gases, Regulated Emissions, and Energy use in Technologies) has been annually updated to model and refine the latest developments in fuels and materials production, as well as vehicle operational and composition characteristics. Updated cradle-to-grave life cycle analysis results from the model’s latest release are described for a wide variety of fuel and powertrain options for U.S. light-duty and medium/heavy-duty vehicles. Light-duty vehicles include a passenger car, sports utility vehicle (SUV), and pick-up truck, while medium/heavy-duty vehicles include a Class 6 pickup-and-delivery truck, Class 8 day-cab (regional) truck, and Class 8 sleeper-cab (long-haul) truck.

In recent studies, optimal control has shown promise as a strategy for enhancing the energy efficiency of connected autonomous vehicles. To maximize optimization performance, it is important to accurately predict constraints, especially separation from a vehicle in front. This paper proposes a novel prediction method for forecasting the trajectory of the nearest preceding car. The proposed predictor is designed to produce short to medium-length speed trajectories using a locally weighted polynomial regression algorithm. The polynomial coefficients are trained by using two types of information: (1) vehicle-to-vehicle (V2V) messages transmitted by multiple preceding vehicles and (2) vehicle-to-infrastructure (V2I) information broadcast by roadside equipment. The predictor’s performance was tested in a multi-vehicle traffic simulation platform, RoadRunner, previously developed by Argonne National Laboratory.

This paper introduces an eco-driving highway cruising algorithm based on optimal control theory that is applied to a conventionally-powered connected and automated vehicle. Thanks to connectivity to the cloud and/or to infrastructure, speed limit and slope along the future route can be known with accuracy. This can in turn be used to compute the control variable trajectory that will minimize energy consumption without significantly impacting travel time. Automated driving is necessary to the implementation of this concept, because the chosen control variables (e.g., torque and gear) impact vehicle speed. An optimal control problem is built up where quadratic models are used for the powertrain. The optimization is solved by applying Pontryagin’s minimum principle, which reduces the problem to the minimization of a cost function with parameters called co-states.

Vehicle lightweighting has been a focus of the automotive industry, as car manufacturers seek to comply with corporate average fuel economy (CAFE) and greenhouse gas (GHG) emissions standards for model year (MY) 2017-2025 vehicles. However, when developing a lightweight vehicle design, the automotive industry typically targets maximum vehicle weight reduction at minimal cost increase. In this paper, we consider the environmental impacts of the lightweighting technology options. The materials used for vehicle lightweighting include high-strength steel (HSS), aluminum, magnesium and carbon fiber reinforced plastic (CFRP). Except for HSS, the production of these light materials is more GHG-intensive (on a kg-to-kg basis) compared with the conventional automotive materials they substitute. Lightweighting with these materials, therefore, may partially offset the GHG emission reductions achieved through improved fuel economy.

While electric vehicles including plug-in hybrid electric vehicles (PHEVs) and battery-powered electric vehicles (BEVs) are considered as promising alternative vehicle/fuel systems to significantly reduce petroleum consumption of the transportation sector, it is important to analyze the emission characteristics and to assess the emission reduction potentials of electric vehicles so that their environmental impacts in terms of climate change, air quality, as well as human health effects could be better understood. To fulfill this objective, we explicitly analyzed the emission characteristics of greenhouse gases (GHG) and criteria air pollutants (CAP, representing VOC, CO, NOx, PM₁₀ and PM₂.₅, and SOx,) of the U.S. power sector, a pivotal upstream sector that impacts the life-cycle GHG and CAP emissions associated with electric vehicles.

To reduce development time and introduce technologies to the market more quickly, companies are increasingly turning to Model-Based Design. The development process - from requirements capture and design to testing and implementation - centers around a system model. Engineers are skipping over a generation of system design processes based on hand coding and instead are using graphical models to design, analyze, and implement the software that determines machine performance and behavior. This paper describes the process implemented in Autonomie, a plug-and-play software environment, to evaluate a component hardware in an emulated environment. We will discuss best practices and show the process through evaluation of an advanced high-energy battery pack within an emulated plug-in hybrid electric vehicle.

The Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) model incorporated fuel economy and electricity use of alternative fuel/vehicle systems simulated by the Powertrain System Analysis Toolkit (PSAT) to conduct a well-to-wheels (WTW) analysis of energy use and greenhouse gas (GHG) emissions of plug-in hybrid electric vehicles (PHEVs). Based on PSAT simulations of the blended charge depleting (CD) operation, grid electricity accounted for a share of the vehicle’s total energy use ranging from 6% for PHEV 10 to 24% for PHEV 40 based on CD vehicle mile traveled (VMT) shares of 23% and 63%, respectively. Besides fuel economy of PHEVs and type of on-board fuel, the type of electricity generation mix impacted the WTW results of PHEVs, especially GHG emissions.

Because Plug-in Hybrid Electric Vehicles (PHEVs) substitute electrical power from the utility grid for fuel, they have the potential to reduce petroleum use significantly. However, adoption of PHEVs has been hindered by expensive, low-energy batteries. Recent improvements in Li-ion batteries and hybrid control have addressed battery-related issues and have brought PHEVs within reach. The FreedomCAR Office of Vehicle Technology has a program that studies the potential benefit of PHEVs. This program also attempts to clarify and refine the requirements for PHEV components. Because the battery appears to be the main technical barrier, both from a performance and cost perspective, the main efforts have been focused on that component. Working with FreedomCAR energy storage and vehicle experts, Argonne National Laboratory (Argonne) researchers have developed a process to define the requirements of energy storage systems for plug-in applications.

Technology change can yield some combination of changes in fuel economy, performance, size, or other characteristics that influence the fleetwide fuel consumption. Thus, engineering progress can be manifest in multiple ways, reflected in the view that technical efficiency may improve even when fuel economy does not. This analysis quantifies underlying technical efficiency gains that reflect trade-offs between fuel economy and other attributes of light vehicles. Prior analyses yielded estimates of technical efficiency trends ranging from 1% per year to nearly 4% per year. Few analyses revealed consistent trends; most identify periods when progress was made at different rates. Using fleetwide (aggregate) data for U.S. light vehicles, exploratory data analysis and correlation techniques were applied to examine trends in energy-related variables. Individual trends for all variables are uneven over time.

A fuel-cycle model-called the Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) model-has been developed at Argonne National Laboratory to evaluate well-to-wheels (WTW) energy and emission impacts of motor vehicle technologies fueled with various transportation fuels. The new GREET version has up-to-date information regarding energy use and emissions for fuel production activities and vehicle operations. In this study, a complete WTW evaluation targeting energy use, greenhouse gases (CO2, CH4, and N2O), and typical criteria air pollutants (VOC, NOX, and PM10) includes the following fuel options-gasoline, diesel, and hydrogen; and the following vehicle technologies-spark-ignition engines with or without hybrid configurations, compression-ignition engines with hybrid configurations, and hydrogen fuel cells with hybrid configurations.

To restrain the phenomenal increase in oil consumption in China, the Chinese government called for measures to reduce oil consumption of the road transportation sector through adopting vehicle fuel consumption standards. This paper describes the development of China's first set of fuel consumption standards for light-duty passenger vehicles. The adopted standards cover M1 class vehicles, which, according to European definition (and adopted by China), include passenger cars, minivans, and sports utility vehicles (SUVs). In particular, we present the goal, technical background, structure, and values of the adopted standards. We also present their potential effect on oil use reduction. The standards are set in liters of fuel consumption per 100 km for individual vehicle weight categories. The standards are maximum fuel consumption values for given vehicle weight categories.

The strong correlation between vehicle weight and fuel economy for conventional vehicles (CVs) is considered common knowledge, and the relationship of mass reduction to fuel consumption reduction for conventional vehicles (CVs) is often cited without separating effects of powertrain vs. vehicle body (glider), nor on the ground of equivalent vehicle performance level. This paper challenges the assumption that this relationship is easily summarized. Further, for hybrid electric vehicles (HEVs) the relationship between mass, performance and fuel consumption is not the same as for CVs, and vary with hybrid types. For fully functioning (all wheel regeneration) hybrid vehicles, where battery pack and motor(s) have enough power and energy storage, a very large fraction of kinetic energy is recovered and engine idling is effectively eliminated.

This paper analyzes fuel economy benefits of direct hydrogen and gasoline reformer fuel cell vehicles, with special focus on cold-start impacts on these fuel cell based vehicles. Comparing several existing influential studies reveals that the most probable estimates from these studies differ greatly on the implied benefits of both types of fuel cell vehicles at the tank-to-wheel level (vehicle-powertrain efficiency and/or specific power), leading to great uncertainties in estimating well-to-wheel fuel energy and/or greenhouse gas (GHG) emission reduction potentials. This paper first addresses methodological issues to influence the outcome of these analyses. With one exception, we find that these studies consistently ignore cold-start and warm-up issues, which play important roles in determining both energy penalties and start-up time of fuel cell vehicles. To better understand cold-start and warm-up behavior, this paper examines approaches and results based on two available U.S.

This paper analyzes four recent major studies carried out by MIT, a GM-led team, Directed Technologies, Inc., and A. D. Little, Inc. to assess advanced technology vehicles. These analyses appear to differ greatly concerning their perception of the energy benefits of advanced technology vehicles, leading to great uncertainties in estimating full-fuel-cycle (or “well-to-wheel”) greenhouse gas (GHG) emission reduction potentials and/or fuel feedstock requirements per mile of service. Advanced vehicles include, but are not limited to, advanced gasoline and diesel internal combustion engine (ICE) vehicles, hybrid electric vehicles (HEVs) with gasoline, diesel, and compressed natural gas (CNG) ICEs, and various kinds of fuel-cell based vehicles (FCVs), such as direct hydrogen FCVs and gasoline or methanol fuel-based FCVs.

This paper assesses the technical potential, costs and benefits of improving the fuel economy of light-duty trucks over the next five to ten years in the United States using conventional technologies. We offer an in-depth analysis of several technology packages based on a detailed vehicle system modeling approach. Results are provided for fuel economy, cost, oil savings and reductions in greenhouse gas emissions. We examine a range of refinements to body, powertrain and electrical systems, reflecting current best practice and emerging technologies such as lightweight materials, high-efficiency IC engines, integrated starter-generator, 42 volt electrical system and advanced transmission. In this paper, multiple technological pathways are identified to significantly improve fleet average light-duty-truck fuel economy to 27.0 MPG or higher with net savings to consumers.

This paper assesses the potential for car and light truck fuel economy improvements by 2010-15. We examine a range of refinements to body systems and powertrain, reflecting current best practice as well as emerging technologies such as advanced engine and transmission, lightweight materials, integrated starter-generators, and hybrid drive. Engine options are restricted to those already known to meet upcoming California emissions standards. Our approach is to apply a state-of-art vehicle system simulation model to assess vehicle fuel economy gains and performance levels. We select a set of baseline vehicles representing five major classes - Small and Standard Cars, Pickup Trucks, SUVs and Minivans - and analyze design changes likely to be commercially viable within the coming decade. Results vary by vehicle type.

This paper describes the integration of a Modal Energy and Emissions Model (MEEM) into a hybrid-electric vehicle simulation model, the PNGV System Analytic Toolkits (PSAT). PSAT is a forward-looking computer simulation model for advanced-technology vehicles. MEEM is a vehicle fuel-consumption and emissions model developed by one of the authors for internal-combustion-engine (ICE) -powered vehicles. MEEM engine simulation module uses a power-demand physical model based on a parameterized analytical representation of engine fuel and emissions production. One major advantage of MEEM is that it does not rely on steady-state engine maps, which are usually not available for most production vehicles; rather, it depends on a list of engine parameters that are calibrated based on regular vehicle dynamometer testing. The integrated PSAT-MEEM model can be used effectively to predict fuel consumption and emissions of various ICE-powered vehicles with both conventional and hybrid power trains.

In recent years, vehicle manufacturers have made great progress in developing and demonstrating commercially available and prototyped hybrid electric vehicles (HEVs). These vehicles include commercially available gasoline hybrid cars (Toyota Prius and Honda Insight) and Partnership for the Next Generation Vehicle (PNGV) diesel hybrid prototypes (Ford Prodigy, GM Precept, and DaimlerChrysler ESX3). In this paper, we discuss tested and claimed fuel benefits and performance of these commercial and prototyped HEVs relative to conventional vehicles (CVs) that are otherwise similar to these HEVs, except for hybridization. We also describe a reverse-engineering approach to de-hybridize or “conventionalize” these five existing commercial and prototyped HEVs. Because these commercial and prototyped HEVs represent a variety of technological choices, configurations, and development stages, this analysis gives us in-depth knowledge about how each of these vehicles achieves high efficiency.

The effects of hybridization on heavy-duty vehicles are not well understood. Heavy vehicles represent a broader range of applications than light-duty vehicles, resulting in a wide variety of chassis and engine combinations, as well as diverse driving conditions. Thus, the strategies, incremental costs, and energy/emission benefits associated with hybridizing heavy vehicles could differ significantly from those for passenger cars. Using a modal energy and emissions model, we quantify the potential energy savings of hybridizing commercial Class 3-7 heavy vehicles, analyze hybrid configuration scenarios, and estimate the associated investment cost and payback time.

Hybrid electric vehicles (HEVs) offer great promise in improving fuel economy. In this paper, we analyze why, how, and by how much vehicle hybridization can reduce energy consumption and improve fuel economy. Our analysis focuses on efficiency gains associated solely with vehicle hybridization. We do not consider such other measures as vehicle weight reduction or air- and tire-resistance reduction, because such measures would also benefit conventional technology vehicles. The analysis starts with understanding the energy inefficiencies of light-duty vehicles associated with different operation modes in U.S. and Japanese urban and highway driving cycles, with the corresponding energy-saving potentials.